Báo cáo hóa học: " Research Article Design and Analysis of an Energy-Saving Distributed MAC Mechanism for Wireless Body Sensor Networks" doc

Hindawi Publishing Corporation
EURASIP Journal on Wireless Communications and Networking
Volume 2010, Article ID 571407, 13 pages
doi:10.1155/2010/571407

Research Article
Design and Analysis of an Energy-Saving Distributed MAC
Mechanism for Wireless Body Sensor Networks
Begonya Otal,1 Luis Alonso,2 and Christos Verikoukis3
1 Department

of Neurosciences, Institute of Biomedical Research August Pi Sunyer (IDIBAPS), 08036 Barcelona, Spain
of Signal Theory and Communications, Universitat Polit`cnica de Catalunya (UPC), 08034 Barcelona, Spain
e
3 Centre Tecnol` gic de Telecomunicacions de Catalunya (CTTC), 08860 Castelldefels, Barcelona, Spain
o
2 Department

Correspondence should be addressed to Begonya Otal, bego
Received 15 February 2010; Revised 26 June 2010; Accepted 17 August 2010
Academic Editor: Edith C.-H. Ngai
Copyright © 2010 Begonya Otal et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The fact that the IEEE 802.15.4 MAC does not fully satisfy the strict wireless body sensor network (BSN) requirements in healthcare
systems highlights the need for the design and analysis of new scalable MAC solutions, which guarantee low power consumption
to all specific sorts of body sensors and traffic loads. While taking the challenging healthcare requirements into account, this
paper aims for the study of energy consumption in BSN scenarios. For that purpose, the IEEE 802.15.4 MAC limitations are first
examined, and other potential MAC layer alternatives are further explored. Our intent is to introduce energy-aware radio activation
polices into a high-performance distributed queuing medium access control (DQ-MAC) protocol and evaluate its energy-saving
achievements, as a function of the network load and the packet length. To do so, a fundamental energy-efficiency theoretical
analysis for DQ-MAC protocols is hereby for the first time provided. By means of computer simulations, its performance is

validated using IEEE 802.15.4 MAC system parameters.

1. Introduction and Related Work
Although the challenges faced by wireless body sensor
networks (BSNs) in healthcare environments are in a certain
way similar to those already existing in current wireless
sensor networks (WSNs), there are intrinsic differences
which require special attention [1]. For instance, human
body monitoring may be achieved by attaching sensors to
the body’s surface as well as implanting them into tissues
for a more accurate clinical practice. Some of these newly
emerged challenges, due to healthcare requirements, range
from low latency and high reliability (i.e., quality of service),
to low power consumption in order to protect human tissue.
Hence, one of the major concerns in BSNs is that of extreme
energy efficiency, which is also the key to extend the lifetime
of battery-powered body sensors, reduce maintenance costs,
and avoid invasive procedures to replace battery in the case
of implantable devices. While taking healthcare requirements
into consideration, in this paper, we concentrate on the evaluation of energy consumption in the Medium Access Control

(MAC) layer. For that purpose, we introduce a new energyefficiency theoretical analysis of a Distributed Queuing MAC
(DQ-MAC) protocol and evaluate its performance under
BSN scenarios. Please note that the optimization design and
evaluation of the here characterized DQ-MAC protocol in
terms of quality of service was presented in [2] under BSN
scenarios considering specific medical settings. The resulted
protocol with integrated cross-layer fuzzy-logic scheduling
techniques was renamed to Distributed Queuing Body Area
Network (DQBAN) MAC protocol. Generally speaking, the

MAC layer is responsible for coordinating channel accesses,
by avoiding collisions and scheduling data transmissions,
to maximize throughput efficiency at an acceptable packet
delay and minimal energy consumption. In this context,
among all IEEE 802 standards available today, the IEEE
802.15.4 (802.15.4) [3] is regarded as the technology of
choice for most BSN research studies [1, 4–7]. However,
even though the 802.15.4 MAC consumes very low power,
the figures may not reach the levels required in BSNs [4, 5].
This is the reason why there exists the need to explore


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EURASIP Journal on Wireless Communications and Networking

other MAC potential candidates for future BSNs [2, 6–18],
which might be potential candidates for other BSN-targeted
standardization bodies, such as the IEEE 802.15.6 task group.
A lot of work has been done on reducing the power
consumption since the first standardization of the 802.15.4
MAC in 2003 [3]. Most of the proposed low-powered MAC
layer protocols are contention based (CSMA) and can be
put into either, one of the two classifications, synchronous
or asynchronous. The basic idea behind a synchronous
MAC is to let the sensors sleep periodically, and to have
them somehow aware of each other’s sleeping schedules. In
order to work efficiently, a very basic requirement is to have
sensors tightly coupled or synchronized to each other. Some
synchronous protocols found in the literature are S-MAC [8]

and T-MAC [9]. S-MAC introduces periodic coordinated
sleep/wakeup duty cycles, and as a result, the battery lifetime
for sensors is increased. One problem of S-MAC is that the
duty cycle requires to be tuned to a specific traffic load.
Thus, its performance suffers under varying traffic loads.
The T-MAC sleeping technique [9] satisfies the varying
traffic requirements. The T-MAC also uses an adaptive
listening technique in neighbor’s transmissions, and sensors
are able to immediately pass the data, avoiding the timeout
introduced in T-MAC. Therefore, as T-MAC duty cycle
varies adaptively, this improves the energy and throughput
performance under varying traffic loads.
The asynchronous techniques employ a completely different approach. They append a long enough preamble to
the data packets that ensures that the destination became
active at least once while the preamble was being transmitted.
B-MAC [10] uses a technique that allows sensors to sleep
without them having to be aware of each other’s schedules
or without being synchronized. It is a simple protocol which
uses long preambles to eliminate the need of synchronization. However, B-MAC somehow suffers from energy
inefficiency due to the new introduced overhead, since all
nodes in the sphere of influence require listening to the long
preambles. X-MAC [11] takes the concepts of B-MAC further and comes up with techniques to reduce the length of the
preamble by putting useful information in the preamble. XMAC avoids overhearing by putting the destination address
in the preamble, that is, unconcerned sensors come to know
just by listening to a part of the preamble that the data
packet is not intended for them, and thus they go back to
sleep. MFP-MAC’s greatest achievement is the reduction of
idle listening and overhearing avoidance in broadcast traffic
[12]. Idle listening is reduced by having the preamble divided
into sequence numbered microframes. This way each sensor

knows when the current data will be put.
The BSN-MAC [6] is a dedicated ultra-low-power adaptive MAC protocol designed for star-based topology BSNs
based on 802.15.4 MAC. By exploiting feedback information
from distributed sensors in the BSN, the BSN-MAC protocol
adjusts protocol parameters dynamically to achieve best
energy conservation on energy-critical body sensors. The
same authors of BSN-MAC published thereafter the H-MAC
[7], which is a novel Time Division Multiple Access (TDMA)
MAC protocol especially designed for biosensors in BSNs. It
improves energy efficiency by exploiting human heartbeat

rhythm information to perform time synchronization for
TDMA. By following the heartbeat rhythm, wireless biosensors can achieve time synchronization without having to turn
on their radio to receive periodic timing information from a
central controller, so that energy cost for time synchronization can be completely avoided and the lifetime of the BSN
can be prolonged. Another energy-efficient TDMA-based
MAC protocol for wireless BSNs is the BodyMAC [13], which
uses flexible and efficient bandwidth allocation schemes and
sleep mode to meet the dynamic requirements of BSNs. In
[13], the authors compared BodyMAC with 802.15.4 MAC.
To reduce energy consumption in a BSN, the authors in [14]
designed a collision-free protocol, where all communication
is initiated by the central node and is addressed uniquely to a
slave node.
All of these protocols try to reduce some of the
commonly identified sources of energy loss: idle listening,
collisions, overhearing, or protocol overhead. On the one
hand, purely contention-based protocols such as S-MAC [8],
T-MAC [9], B-MAC [10], X-MAC [11], and MFP-MAC [12]
are not energy-efficient enough for real-time monitoring

applications in BSNs. On the other hand, the problem with
TDMA-based protocols might be the bandwidth under
utilization whenever there is a BSN with heterogeneous
traffic. This is the main reason why we suggested the use of
the DQ-MAC family, which grants immediate access for light
traffic loads and seamlessly moves to a reservation system
for high traffic loads, eliminating collisions for all data
transmissions. The optimisation introduced in [2] proves
how DQBAN is able to cope with constant and heterogeneous traffic in two different medical scenarios under BSNs,
assuming that the central node is unconstrained in energy
(e.g., central care unit) and is always reachable (see Figure 1).
DQBAN energy-efficiency performance remains the same
as the one analyzed in this paper without the optimization
introduced in [2]. Here, apart from providing a new
theoretical energy-consumption analysis in nonsaturation
conditions, an energy-efficiency comparison between DQMAC performance and that of the standard facto 802.15.4
MAC and the BSN-MAC is portrayed. BSN-MAC has been
selected as a reference benchmark for its similarity in terms
of design as well as structure, and studied scenarios in [6].
Current 802.15.4 MAC limitations for BSNs are formulated in Section 2. Section 3 follows with a brief overview
of the most relevant specifications regarding DQ-MAC
protocols. Section 4 introduces significant DQ-MAC protocol enhancements to minimize energy consumption in
BSNs. The newly proposed energy-efficiency analysis in nonsaturation conditions and the adopted energy-aware radio
activation policy are presented in Section 5. The model
validation and the performance evaluation by means of
computer simulations are shown in Section 6. The last
section concludes the paper.

2. 802.15.4 MAC Limitations in
Healthcare Scenarios

The 802.15.4 MAC accepts three network topologies: star,
peer to peer, and cluster tree. Our focus is here on 1-hop


EURASIP Journal on Wireless Communications and Networking
star-based BSNs, where a body area network (BAN) coordinator is elected. In a hospital BSN, the BAN coordinator can
be a central care unit linked to a number of ward patients
wearing several body sensors (see Figure 1). Communication
from body sensors to BAN coordinator (uplink), from
BAN coordinator to body sensors (downlink), or even from
body sensor to body sensor (ad hoc) is possible. In the
following, we study uplink and downlink communications,
which occurs more often than ad hoc communication for
regular patient monitoring BSNs. In a 802.15.4 star-based
network, the beacon mode appears to allow for the greatest
energy efficiency. Indeed, it allows the transceiver to be
completely switched off up to 15/16 of the time when
nothing is transmitted/received, while still allowing the
transceiver to be synchronized to the network and able to
transmit or receive a packet at any time [19]. The beacon
mode introduces the so-called superframe structure. The
inter-beacon period is partially or entirely occupied by the
superframe, which is divided into 16 slots. Among them,
there are at most 7 guaranteed time slots (GTS), (i.e., they
are dedicated to specific nodes), which form the contentionfree period (CFP) [3]. This functionality targets very lowlatency applications, but it is not scalable in BSNs, since
the number of dedicated slots is not sufficient [4]. Further,
most of the time, a device uses only a small portion of the
allocated GTS slots, and the major portion remains unused,
resulting in empty holes within the CFP (i.e., bandwidth
under utilization). In the medical field, where one illness

usually boost up other illnesses, many body sensors should
be able to reach the BAN coordinator via such guaranteed
services [5].
In such conditions, the use of the contention access
period (CAP) is required, where channel accesses in the
uplink are coordinated by a slotted carrier sense multiple
access mechanism with collision avoidance (CSMA/CA).
Nevertheless, it has already been proved that the CSMA/CA
mechanism, used within the CAP, has a significant negative
impact on the overall energy consumption, as the traffic
load in the network steadily increases [19–21]. In [19], the
authors suggested that physical level improvements, such as
energy-aware radio activation policies, should be combined
with other MAC optimizations to allow for more energyefficient wireless networks. Thus, the appraisal of other
existing MAC protocols in terms of effective energy per
information bit introduces important challenges in BSNs.
This is the reason why we here introduce energy-aware radio
activation policies into a high-performance MAC protocol
different from CSMA/CA, while analyzing and evaluating
its energy-saving performance in BSNs. In the literature, it
is already possible to find some research work on reducing
the power consumption of the standard de facto 802.15.4
MAC in BSN scenarios [6, 7]. The Body Sensor Network
MAC (BSN-MAC) is based on 802.15.4 MAC supporting
both star and peer-to-peer network topologies. The authors
in [6, 7] concentrate also on a 1-hop star-based topology,
since in their analyzed BSN, the number of sensors is
limited and an external mobile device, such as PDA or cell
phone, acts as a BAN coordinator. However, the promising
accomplishment of the DQBAN protocol in terms of quality

3

of service under healthcare requirements in hospital settings
[2], evokes the idea to further explore and analyze the
energy-efficiency of this family of DQ-MAC protocols (i.e.,
[2, 15–18]) in general BSN scenarios. In [15–18], DQMAC favorable behavior (especially versus CSMA/CA) is
achieved thanks to the inherent protocol performance at
eliminating collisions in data transmissions and minimizing
the overhead of contention procedures (i.e., carrier sensing
and backoff periods). Based on that, we propose here a novel
DQ-MAC energy-efficiency theoretical analysis for nonsaturation conditions and evaluate its performance in front
of 802.15.4 MAC and BSN-MAC in BSN scenarios, bearing
medical applications in mind. Please note that in order to
cope with healthcare stringent requirements of quality of
service, the same authors introduced new cross-layer fuzzylogic scheduling techniques into the here proposed DQMAC protocol and evaluate its performance in terms of
reliability and maximum latency in different hospital settings
[2]. The results proved to show the suitability of the here
presented DQ-MAC protocol in more specific healthcare
BSN scenarios.

3. Overview of Distributed
Queuing MAC Protocols
This section highlights the basic features related to DQ-MAC
protocols that are essential for the understanding of the new
energy-saving enhancements and energy-efficient theoretical
analysis proposed in this paper. The introduction of the
Distributed Queuing Random Access Protocol (DQRAP)
for local wireless communications was already presented
in [15] and later in [16] under the name of Distributed

Queuing Collision Avoidance (DQCA), as an adaptation to
IEEE 802.11b MAC environments. It has already been shown
that the throughput performance of a DQ-MAC protocol
outperforms CSMA/CA in all studied scenarios. The main
characteristic of a DQ-MAC protocol is that it behaves as a
random access mechanism under low traffic conditions, and
switches smoothly and automatically to a reservation scheme
when the traffic load grows, that is, DQ-MAC protocols show
a near-optimum performance independent of the amount of
active terminals and traffic load.
Let us consider a star-based topology with several nodes
and a network coordinator, following DQRAP original
description [17], the time axis is divided into an “access
subslot” that is further divided into access minislots (m)
and a “data subslot.” The basic idea is to concentrate
user access requests in the access minislots, while the “data
subslot” is devoted to collision-free data transmissions (see
Figures 2 and 3). The DQRAP analytical model approaches
the delay, and throughput performance of the theoretical
is in charge of the data server (the “data subslot”). This
provides a collision resolution tree algorithm that optimum queuing systems M/M/1 or G/D/1, depending on
the traffic distribution. Hence, DQ-MAC protocols can be
modeled as if every station in the system maintains two
common logical distributed queues—the collision resolution
queue (CRQ) and the data transmission queue (DTQ)—
physically implemented as four integers in each station; two


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EURASIP Journal on Wireless Communications and Networking

station-dependant integers that represent the occupied position in each queue; two further integers shared among all
stations in the system that visualize the total number of stations in each queue, CRQ and DTQ (see Figure 4). The CRQ
controls station accesses to the collision resolution server
(the access minislots), while the DTQ proves to be stable for
every traffic load even over the system transmission capacity.
Note that the number of access minislots is implementation
dependant, but we are formally using 3 access minislots,
following the original DQRAP structure and argumentation
for maximizing its throughput performance [17].
A DQ-MAC protocol consists of several strategic rules,
independently performed by each station by managing the
aforementioned four integers (i.e., corresponding to the two
distributed queues, CRQ and DTQ) [17], which answer
(i) “who” transmits in the data slot and “when”,
(ii) “who” sends an access request sequence in the
minislots (m) and “when” and
(iii) “how” to actualize their positions in the queues.

4. DQ-MAC Energy-Saving
Enhancements for BSNs
Figure 2 shows the superframe format of a DQ-MAC protocol proposal in a possible star-based BSN scenario. There
might be several ward patients wearing a number of body
sensors that communicate to a central care unit (i.e., BAN
coordinator), as portrayed in Figure 1. The complete energysaving superframe structure comprises two differential parts:
(i) from body sensors to BAN coordinator (uplink), with
a CAP and a CFP. The CAP is further divided into
m access minislots, whereas the CFP is devoted to
collision-free data packet transmissions;

(ii) from BAN coordinator to body sensors (downlink)
using the feedback frame, which contains several
strategic fields.
In fact, the DQ-MAC superframe is bounded by the feedback
packet (FBP) contained in the feedback frame as portrayed in
Figures 2 and 3 which is broadcasted by the BAN coordinator.
Similar to the 802.15.4 MAC superframe format, one of
the main uses of the FBP is to synchronize the attached body
sensors to the BAN coordinator. The FBP always contains
relevant MAC control information (i.e., corresponding also
to the protocol rules), which is essential for the right
functioning of all body sensors in the BSN. When a body
sensor wishes to transfer data, it first waits for the FBP.
After synchronization, it independently actualizes the integer
counters, by applying a set of rules that determine its position
in the protocol distributed queues, CRQ and DTQ (see
Figure 4). At the appropriate time, the body sensor transmits
either an access request sequence (ARS), of duration tARS ,
in one of the randomly selected access minislots (within the
CAP), or its data packet in the contention-free data slot
of duration tDATA (within the CFP). The BAN coordinator
may acknowledge the successful reception of the data packet
by sending an optional acknowledgment frame (ACK). This

sequence is summarized in the energy-saving DQ-MAC
superframe depicted in Figure 3.
All in all, the main differences of this energy-saving DQMAC superframe format in Figure 3 with respect to the
previous DQ-MAC protocols [9–18] the following:
(1) A preamble (PRE) is newly introduced within the
broadcasted feedback frame, concretely between the

ACK and the FPB, to enable synchronization after
power-sleep modus (i.e., either idle or shutdown, see
[19, 22]). The intuitive reasoning is the following: (i)
the feedback frame is an aggregation of an ACK and
the FBP in order to save PHY header overhead and
therefore energy consumption at reception, that is,
the ACK is essential only to the body sensor, which
transmitted in the previous contention-free data slot.
Hence, body sensors can prolong their power-sleep
modus until the immediate reception of the FBP; (ii)
the precise position of the PRE between the ACK
and the FBP is mainly due to scalability in terms
of energy efficiency. This means that in a future
system design, several ACKs may be aggregated just
before the preamble (PRE). Body sensors within the
DQ-MAC system not being addressed in this multicast/aggregated communication shall only receive the
FBP. This is the reason why a preamble is suitable in
this explicit position.
(2) FBP is here of fixed length (i.e., independent of the
number of sensors in the BSN) and contains two
new fields for specific energy-saving purposes, the
modulation and coding scheme (MCS) and length of
the packet being transmitted in the next contentionfree data slot (i.e., in the next CFP). This facilitates
independent energy-aware radio activation policies,
so that body sensors can calculate the time they can
remain in power-sleep modus (see [22]). Further,
the MCS field is also thought for future multirate
medical applications in BSNs (i.e., scalability in terms
of application-oriented medical body sensors).
Note that the FBP always contains a specific field

named QDR (Queuing Discipline Rules), which contains
the updating information regarding the aforementioned
ARS (see [15–18]). Additionally, there is the possibility to
transmit data packets of variable length (tDATA ), using the
same frame structure, at the same time that energy-saving
benefits are maintained, which means a flexible CFP.
It must be pointed out that a similar DQ-MAC superframe format approach using the preamble and the abovedepicted FBP have already been proposed by the same
authors in [2, 23], though studied in totally different
scenarios and conditions. In [2], the DQBAN protocol
commitment is to guarantee that all packet transmissions
are served within their particular application-dependant
quality-of-service requirements (i.e., reliability and message
latency), without endangering body sensors battery lifetime within BSNs in medical scenarios. For that purpose,
the authors propose a cross-layer fuzzy-logic scheduling
algorithm to deal with multiple cross-layer input variables


EURASIP Journal on Wireless Communications and Networking
Patient
Patient

Body sensor

Body sensor

Patient
Body
sensor

Care unit

BAN
coordinator
Patient
Patient
d < 8m
Body sensor
Body sensor

Figure 1: A star-based BSN in a healthcare scenario.

Feedback
frame
Contention
access period
Contention
free period
Time axis

Time axis

Figure 2: DQ-MAC protocol frame format and time sequence.

of diverse nature in an independent manner. Note that
the introduction of the fuzzy-logic techniques does not
change the energy-consumption performance of the depicted
protocol [2]. In [23], a preliminary analytical evaluation of
the enhanced DQ-MAC protocol is presented under general
WSN scenarios in saturation conditions. Though following
the same line as [2, 23] in terms of DQ-MAC energy-saving
superframe format, this paper aims to analyze the nonsaturation DQ-MAC energy-efficiency performance in BSNs,

mainly completing the work in [2] in a broadened scenario.

5. Non-Saturation DQ-MAC
Energy-Efficiency Analysis
Without loss of generality, it is now considered that all
body sensors in our studied scenario (see Figure 1) generate Poisson-distributed data messages, whose length is an
exponential random variable with average (1/μ) · Lbit bits.
Recall from Figure 3 the DQ-MAC superframe structure,

5

and notice that Lbit is the payload length within the CFP
expressed in bits. All body sensors generate here the same
average traffic load, and the total packet arrival rate is
λ (packets/superframe), where we define “packet” as the
fraction of a message of length Lbit in bits. The average
service rate of the system is further explained thereafter
and denoted by μ (packets/superframe). For this theoretical
analysis, we use the whole DQ-MAC superframe duration as
the time unit, and we denote N by the number of DQ-MAC
superframe units (see Figure 4).
As previously mentioned, a DQ-MAC statistical model
approaches the delay and throughput performance of the
theoretical optimum queuing systems M/M/1, or G/D/1,
depending on the traffic distribution (i.e., M: exponential,
G: general, and D: deterministic). DQ-MAC protocol can
be modeled as if every body sensor in the system maintains
two common logical distributed queues—CRQ and DTQ—
as portrayed in Figure 4. The CRQ controls body sensor
accesses to the collision resolution server (the access minislots) and is designed to resolve collisions among stations

attempting to successfully obtain an access minislot. The
DTQ, in charge of the data server (the “data subslot”), is used
to buffer the data packets that have obtained permission to
transmit and are awaiting their scheduled time of departure
using a first-come-first-served (FCFS) discipline. The enable
transmission interval (ETI), modeled with a nonqueuing
infinite server system in Figure 4, is the time elapsed from
the actual arrival time of a packet to the head of the
CRQ subsystem at the beginning of the next DQ-MAC
superframe, when the contention process can start. The first
queuing system models the CRQ subsystem and the second
represents the DTQ subsystem.
5.1. DQ-MAC Model (M/M/1). DQ-MAC energy-efficiency
analysis applying Markov queuing theory can only be done
in stable conditions, that is., when the input rate of a
system denoted by λ (packets/superframe) is at most equal
to the average service rate of the system denoted by μ
(packets/superframe), that is, the stability condition can
be expressed as λ/μ < 1. Otherwise, the system becomes
unstable and the queue might grow indefinitely, that is, not
all arrivals are eventually served. The M/M/1 queuing model,
with an interarrival and service-time distribution exponential, and an infinite queuing server, is considered as one of
the simplest birth-death processes. As aforementioned, DQMAC can be modeled as a queuing system that consists of
two statistical queuing subsystems. The CRQ subsystem is
evaluated using M/M/1 Markov chain. The DTQ subsystem
is modeled as a G/D/1 [15].
Here, the input rate λ is the ratio of the average number of
newly arrived packets of Lbit bits—generated in messages—
per DQ-MAC superframe unit (N). The average service rate
μ is the ratio of served packets per DQ-MAC superframe unit

and is computed as

μ = ln

1
,
1 − p(λ)

(1)


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EURASIP Journal on Wireless Communications and Networking
Time axis
Feedback frame

CAP

CFP

m
access
minislots

Variable length

ACK

Contention free data slot

tARS

New frame
starts

Fixed length

PRE

FBP

taw

tdata

IFS
QDR Lgth MCS

From body sensor to BAN coordinator
(uplink)

From BAN coordinator to
body sensor (downlink)

Figure 3: Detailed new energy-saving DQ-MAC superframe for BSNs.

DTQ subsystem

CRQ subsystem

λ

Access
minislots

ETI
CRQ

Data
slot
DTQ

m

Access
minislots
m
Time axis

Contention free
data slot
Time axis

N DQ-MAC superframes

DQ-MAC superframe

Figure 4: DQ-MAC system model and superframe relation.

where p(λ) is the probability to find successfully an empty

access minislot. We can with confidence make the assumption
that the input traffic follows a Poisson process with input
rate λ and that the CRQ service time for a packet follows an
exponential distribution with average service rate μ, as shown
in [18]. It is also possible to see that the input rate of the DTQ
subsystem is λDTQ = λ, for m ≥ 3, and the average service
rate μDTQ = 1, that is, G/D/1.
Based on the delay analysis approach of [18], we define
here the DQ-MAC system delay with the term Ndelay as
the total number of DQ-MAC superframes a body sensor
remains in the DQ-MAC system for each specific packet it
requires to transmit. First, let us consider a residual time in
ETI NETI (expressed in number of DQ-MAC superframes),
waiting for a new DQ-MAC superframe, where a body sensor
may send an ARS within the access minislots. In case of
collision, the body sensor remains in CRQ until it is the turn
to transmit an ARS in another access minislot. Hence, NCRQ ,
expressed in DQ-MAC superframes, is the CRQ waiting

plus the service time (CRQ subsystem). Similarly, NDTQ
represents the DTQ waiting time plus the DTQ service time
in DQ-MAC superframes (DTQ subsystem). So, the average
total delay E[Ndelay ] a body sensor’s packet remains in DQMAC system model can be computed as
E Ndelay = E[NETI ] + E NCRQsubsys + E NDTQsubsys ,

(2)

where for each packet,
(i) E[NETI ] is the average residual DQ-MAC superframes
in ETI (i.e., by default 0.5 units [18]),

(ii) E[NCRQsubsys ] is the average number of DQ-MAC
superframes in the CRQ subsystem, and
(iii) E[NDTQsubsys ] is the average number of DQ-MAC
superframes in the DTQ subsystem.
Further, based on the delay model of DQ-MAC protocol
in [18], we can treat CRQ as an M/M/1 system. Thus,


EURASIP Journal on Wireless Communications and Networking
applying the average service rate μ of (1) and the input rate
λ to the M/M/1 queue, we achieve the average delay of the
CRQ subsystem E[NCRQsubsys ] as
E NCRQsubsys =

1
ln 1/ 1 − p(λ)

−λ

.

(3)

In [18], it is also proved that the input traffic process of
DTQ, or say the output traffic of CRQ, is a Poisson process.
It is also assumed that the corresponding size of the here
depicted DQ-MAC superframe is 1. Hence, for DTQ, the
service time for a packet is constant, one packet per DQMAC superframe. So, following M/D/1 queue analysis [18],
we obtain E[NDTQsubsys ] immediately,
E NDTQsubsys = 1 +

λDTQ
λ
,
=1+
2 1 − λDTQ
2(1 − λ)

(4)

where the input rate of the DTQ subsystem is λDTQ = λ for
m ≥ 3, as aforementioned.
5.2. Energy-Aware Radio Activation Policy. Figure 5 illustrates the energy-aware radio activation policy following
DQ-MAC adapted energy-saving superframe format as in
Figure 3. This allows different power management scenarios
of body sensors using DQ-MAC under BSNs. Note that each
body sensor synchronizes to the BSN thanks to the novel
preamble sequence (PRE) of duration tPRE after a period
in idle mode. Thereafter, it receives the required system
information via the FBP of duration tFBP for updating its
distributed queues, CRQ and DTQ [15]. After each FBP, a
short interframe space tIFS is left for processing purposes
like in 802.15.4 [3]. Active body sensors involved in the
access procedure like in scenarios (1) and (2) start by
sending an ARS, here of duration length tARS , in one of the
randomly selected access minislots [15]. Prior to that, these
body sensors should have switched their radio from idle to
transmit mode, which take them a transition time tia for body
sensor radio wakeup (i.e., from idle to active modes [19]).
Next, scenario (3) depicts the transmission of a previously

granted packet of duration length tDATA preceded by the
transition time tia . If the packet is received correctly, an ACK
of duration tACK is sent back to the transmitting body sensor
together with the FBP after a maximum time taw − tACK ,
during which the receiver turns its radio to idle mode to save
energy.
In [3], taw is characterized as the maximum time to wait
for an ACK. Scenario (4) shows how an active body sensor
waiting in idle mode synchronizes through the preamble
sequence to receive the FBP. Finally, scenario (5) portrays
how a body sensor in shutdown state wakes up and waits for
some time in idle mode to synchronize through the preamble
and get the FBP to update the state of its CRQ and DTQ
queues [15].
5.3. Energy-Efficiency Theoretical Analysis. Let us first define
Ptx , Prx and Pidle as the power consumption (in W) in
transmit, receive and idle modes respectively and, similarly
E[ttx ], E[trx ] and E[tidle ] as the average time in seconds

7

a body sensor spends in each of the aforementioned
modes within the queuing subsystems, CRQ and DTQ (see
Figure 4). Further, we define E[Nwaiting ] as the average total
number of DQ-MAC superframes waiting in the whole
queuing system (i.e., CRQ and DTQ), and E[NARS tx ] as the
average number of DQ-MAC superframes required in the
CRQ subsystem to transmit a successful ARS.
Thus, the average consumed energy per information bit
(J/bit) E[εbit ] for every active body sensor in the BSN can be

expressed as
E[εbit ] =

E ε Superframe
Lbit

,

(5)

where Lbit corresponds to the payload data length in bits, and
E[εSuperframe ] as
E εSuperframe = Ptx E[ttx ] + Prx E[trx ] + Pidle E[tidle ],

(6)

where
E[ttx ] = E[NARS tx ](tARS + tia ) + E[TDATA ] + tia ,
E[trx ] = E Nwaiting (tPRE + tFBP + tia ) + tACK ,
E[tidle ] = E Nwaiting

(7)

E tSuperframe − tPRE + tFBP

+ E[NARS tx ] E tSuperframe
(8)
− (tARS + tia + tPRE + tFBP )

+ E tSuperframe − (E[tDATA ] + tPRE + tFBP ) .

Further, the duration of the time DQ-MAC superframe
tSuperframe in seconds derived from Figure 3 is characterized
as
tSuperframe = mtARS + tDATA + taw + tPRE + tFBP + tIFS ,

(9)

where m corresponds to the number of minislots used in
the DQ-MAC protocol and tARS , tDATA , taw , tACK , tPRE , tFBP ,
tIFS , and tia have been previously described following the
illustration example of power management scenarios in
Figure 5.
Following the aforementioned assumption that the arriving traffic λ follows a Poisson distribution in both CRQ and
DTQ subsystems, we have that the probability of finding an
empty access minislot in the CRQ subsystem is
P(λ) = e−λ/m ,

(10)

where m corresponds to the number of access minislots
used in the DQ-MAC protocol. This result can be explained
intuitively; if the input rate to the CRQ system is λ, then
the load to each access minislot is λ/m. So the probability of
finding an empty access minislot is e−λ/m . Now, considering


8

EURASIP Journal on Wireless Communications and Networking

the previously-presented system delay analysis derived from
[18], we define E[Nwaiting ] as
E Nwaiting = E[NETI ] + E NCRQ + E NDTQ ,

(11)

where
(i) E[NETI ] here outlines the average number of residual
DQ-MAC superframes waiting in idle mode, which is
equivalent to the previously defined E[NETI ];
(ii) E[NCRQ ] denotes the average number of DQ-MAC
superframes waiting in idle mode in the CRQ based
on M/M/1 queuing model, which corresponds to the
total number of DQ-MAC superframes in the CRQ
subsystem, E[NCRQsubsys ], minus the number of DQMAC superframes required to transmit all ARS (see
(3));
(iii) E[NDTQ ] represents the average number of DQMAC superframes waiting in the DTQ subsystem
based on M/D/1 queuing model [18], which is
the total number of DQ-MAC superframes in the
DTQ subsystem, E[NDTQsubsys ], minus 1 DQ-MAC
superframe used to transmit the data payload (see
(4)).

E[NETI ] = 0.5,
1
ln 1/ 1 − p(λ)

E NDTQ =

6. Model Validation and

Performance Evaluation
The performance of the previously studied DQ-MAC
energy-efficiency analysis is validated first with an analytical
representation of the proposed model and thereafter via
MATLAB computer simulations as following
(i) The energy-efficiency analytical DQ-MAC model in
non-saturation conditions is compared to 802.15.4
MAC energy-consumption analysis presented by
Bougard in [19] and a state-of-the-art energy-saving
BSN-MAC [6].
(ii) Computer simulations are further performed, by
implementing DQ-MAC protocol strategic rules
from [9], within a star-based BSN, as the one
portrayed in Figure 1.

Hence,

E[NETI ] =

Following (10), we defined p(λ) as the probability of
finding an empty access minislot assuming that the arriving
traffic λ follows a Poisson distribution in the CRQ subsystem,
that is, if a body sensor does not succeed in sending an ARS
in an empty access minislot with probability p(λ) the first
time, the second time is with probability p(λ/m), the third
time with probability p(λ/m2 ) and so on. This is the inherent
behavior of a DQ-MAC protocol, because only the body
sensors occupying the same position in the CRQ subsystem
compete for the one of the m access minislots at a time (see
Figure 4) [17, 18].

−λ

− (E[NARS tx ] − 1),

(12)

λ
.
2(1 − λ)

Eventually, E[NARS tx ] denotes the average number of
time frames used to transmit all required ARS during the
waiting time in the CRQ system, before a sensor grants its
access into the DTQ system. Based on the CRQ subsystem
represented in Figure 4, we characterize E[NARS tx ] here as,
E[NARS tx ] = 1p(λ) + 2 1 − p(λ) p

λ
m

+ 3 1 − p(λ)

1− p

λ
m

+ 4 1 − p(λ)

1− p

× 1− p
∞

=

⎡
⎣ip

i=1
∞

=
i=1

λ
mi−1

⎡
⎣ie

λ
m2

λ/mi

p
i−1
k=1

i−1

1−e
k=1

λ
m2

λ
+ ···
m3
λ
1− p
mk−1
⎤
λ/mk

⎦.

(i) Scenario 1 is a comparison of the analytical results
in a high density area (i.e., 80% traffic load). In
this scenario, we study the energy consumption
depending on the payload length.
(ii) Scenario 2 portrays the analytical and simulation
results under increasing relative traffic loads. In this
scenario, we choose the longest data payload lengths
(L) of 80, 100, and 120 bytes, to minimize the PHY
(6 bytes) and MAC (8 bytes) headers overhead per
information bit.

λ
m

p

6.1. Scenario Description. The reference scenario is defined
by the system parameters corresponding to the standardized
802.15.4 MAC default values in the upper frequency band
2.4 GHz at the fixed data rate 250 Kb/s [3]. Based on the
illustration scenario in Figure 1, we study the following
scenarios:

(13)
⎤
⎦

A body sensor waits for an ACK (11 bytes) for a
maximum time of taw − tACK , where taw is limited to 864 μs,
as defined in [3]. Thereafter, the synchronization preamble
sequence (PRE) corresponding to 4 bytes is followed by the
FBP of 11 bytes, similar to a beacon frame in [3]. We use m =
3 access minislots, like in [2, 15–18], and the ARS duration
tARS is equivalent to the Preamble sequence in 802.15.4 MAC
(see Table 1).


EURASIP Journal on Wireless Communications and Networking

9

Table 1: IEEE 802.15.4 MAC parameter values.
Parameter

Value

Parameter

Value

PHY header

6 bytes

ACK

11 bytes

MAC header

9 bytes

Beacon

11 bytes

Data payload

20 to 120 bytes

taw

864 μs

Data rate

250 Kb/s

tIFS

192 μs

Preamble

4 bytes

m

3

FBP

11 bytes

tARS

128 μs

DQ-MAC

Table 2: IEEE 802.15.4 transceiver power consumption (−5 dBm).
Ptx
22.09 mW

Prx
35.23 mW

Pidle
712 μW

In order to make a fair comparison, all used transceiver
power consumption values are formalized as in [19, 22]
(see Table 2). Note that the power consumption in transmission mode is for a transmit power of −5 dBm, which
is the value used in [19] analytical results, which we
use for our comparison with DQ-MAC energy-efficiency
analysis.
6.2. Channel Modeling and Time-Coherence Assumption.
Every active body sensor is supposedly located at a random
distance d from the BAN coordinator, as portrayed in
Figure 1. The channel link implementation is based on the
path loss model of the 802.15.4 standard [3], where the
average received power is expressed as a function of an
arbitrary T-R separation distance d < 8 meters (i.e., within
a hospital setting). In our simulations, the time-variant
received signal also includes additive white Gaussian noise
(AWGN) and the effect of log-normal shadowing, assuming that the channel is coherent within the transmission
of a DQ-MAC superframe, like in indoor environments
[24].
6.3. Scenario 1: Analytical Result Comparison (High-Density
Area). Analytical results for a high-density area (80% traffic

load) are here compared between the DQ-MAC energy
consumption analytical model, the 802.15.4 MAC energyconsumption analysis presented by Bougard in [19], and
the BSN-MAC protocol developed by the authors in [6].
This BSN-MAC protocol is used as a second reference
benchmark besides the standard de facto 802.15.4 MAC,
since it is a state-of-the-art energy-saving MAC proposal
for BSN environments. In the energy-efficiency analysis,
the authors of 802.15.4 MAC model [19] and BSN-MAC
model [6] focus on a general1-hop star-based wireless sensor
network under high traffic conditions. We have used the
energy-efficiency model from [19] and the BSN-MAC model
from [6], using adaptively beacon orders up to 12, in order

to be able to fairly compare 2 different models with our
here proposed DQ-MAC model. Our aim is to evaluate the
energy consumption per information bit, which is defined
as the ratio of the average total energy-consumption per
body sensor and per payload length (i.e., information bit).
The results portrayed in Figure 6 follow the axis description:
The x-axis represents the payload length which increases
until 120 bytes (see Table 1). In the y-axis, we evaluate the
energy consumption per information bit following DQMAC theoretical analysis (see (5)) in our BSN scenario.
The energy consumption is computed considering each body
sensor time and power consumption in each of these states in
non-saturation conditions. Figure 6 portrays the analytical
results of the energy consumption per information bit of
the here presented DQ-MAC model (see (5)) versus the
802.15.4 MAC model analyzed in [19] and the BSN-MAC
protocol developed by the authors in [6], as the packet
payload load increases in the x-axis. Different curves are

shown for a traffic load of 80% (i.e., high-density area).
It can be seen that
(i) BSN-MAC outperforms IEEE 802.15.4 MAC in
19.09% for payload length packet of 50 bytes;
(ii) DQ-MAC outperforms IEEE 802.15.4 MAC in
43.31% for a payload length packet of 50 bytes;
(iii) BSN-MAC outperforms IEEE 802.15.4 MAC in
7.20% for payload length packet of 80 bytes;
(iv) DQ-MAC outperforms IEEE 802.15.4 MAC in
36.65% for a payload length packet of 80 bytes.
We conclude that DQ-MAC is superior to both the
standard 802.15.4 and the BSN-MAC in terms of energy
consumption for high traffic loads (i.e., 80% traffic load) and
all packet lengths. This can be explained by understanding
the inherent DQ-MAC behavior of avoiding collisions in data
transmissions, idle listening, and overhearing, at the cost of
some small protocol overhead, which remains invisible for
high traffic loads. Thus, DQ-MAC reduces the most critical
energy-consumption features of other state-of-the-art MAC
protocols under hightraffic conditions, and for this reason it


EURASIP Journal on Wireless Communications and Networking

taw

(1)

Data

FBP

ARS
ARS
ARS

FBP

Body sensor
(1) (access)

PRE

Sync

ACK

Access
minislots
m

PRE

10

tIFS

tIFS

Time

Power
consumption

Data

FBP

ACK

ARS
ARS
ARS

FBP

PRE

Body sensor
(2) (access)

PRE

taw

(2)

tIFS

tIFS

Time
Power
consumption

Data

FBP

ACK

ARS
ARS
ARS

FBP

PRE

Body sensor
(3) (data)

PRE

taw

(3)

tIFS

tIFS

Time
Power
consumption

Data

FBP

ACK

ARS
ARS
ARS

FBP

PRE

Body sensor
(4) (idle)

PRE

taw

(4)

tIFS

tIFS

Time
Power
consumption

Data

FBP

ACK

ARS
ARS
ARS

FBP

PRE

Body sensor
(5) (shutdown)

PRE

taw

(5)

tIFS

tIFS

Time
Power
consumption

Chip wake-up
Idle
Radio wake-up (tia )

Receive
Transmit

Figure 5: Power management scenarios for different body sensors using DQ-MAC.

might be a suitable candidate for star-based BSNs in medical
settings, completing our work in [2].
6.4. Scenario 2: Analytical and Simulation Results. Analytical
and simulation results are portrayed under increasing traffic

loads. We compare first our here presented DQ-MAC
energy-consumption analytical model with the 802.15.4
MAC energy-consumption analysis presented by Bougard in
[19]. Thereafter, the DQ-MAC analytical model is evaluated
by MATLAB computer simulations.


×10−7
14

×10−7

12
10
19.09%
8
7.2%

6

36.65%

4
43.31%
2
20

11

Energy consumption per information bit (J/bit)

Energy consumption per information bit (J/bit)

EURASIP Journal on Wireless Communications and Networking

30

40

50 60 70 80 90
Payload length (bytes)

100

110

120

IEEE 802.15.4
BSN-MAC
DQ-MAC

5.5
5
4.5
4
36.65%

3.5
3
2.5
2
1.5
0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Relative traffic load
802.15.4 (L = 80 bytes)
802.15.4 (L = 100 bytes)
802.15.4 (L = 120 bytes)

DQ-MAC (L = 80 bytes)
DQ-MAC (L = 100 bytes)
DQ-MAC (L = 120 bytes)

Figure 6: Analytical energy consumption per information bit
(high-density area).

Figure 7: Analytical energy consumption per information bit: DQ
versus IEEE 802.15.4 MAC.

The results portrayed in the succeeding figures follow the
axis description hereafter.

As previously analyzed, DQ-MAC achievement is most
relevant for high traffic loads than for low traffic loads.
Remember though that DQ-MAC may behave adaptively,
that is, granting immediate access for light traffic loads and
seamlessly moving to a reservation system (i.e., CRQ and
DTQ) for high traffic loads. Again this shows the good
inherent performance of the protocol also in terms of energyconsumption.
The same scenario of Figure 7 is now depicted in Figure 8
as a comparison between the aforementioned DQ-MAC analytical model (see (5)), and the simulated results from DQMAC energy consumption per information bit. As before, in
the x-axis, the relative traffic load in the system increases, and
different curves are shown for data payload lengths (L) of 80,
100 and 120 bytes. Here, the excellent protocol performance
can be seen even for the highest traffic load between 80% and
90%, which remains under 350 nJ/bit. Further, simulation
results prove the right theoretical analysis of the protocol
performance in terms of energy efficiency. Needless to say,
the energy consumption per information bit tends to be
minimized by using the maximum packets lengths allowed in
the standard. Simulations results corroborate also this fact.
In order to further evaluate the energy-consumption
performance of the whole DQ-MAC queuing system, we
study the time spent in each of the activity modes, that
is, transmit, receive and idle modes, separately. Figure 9
shows that the transmit and receive time remains practically
constant for all traffic loads, with the exception of the receive
time for very high traffic loads (i.e., λ ≥ 85%). However,
when the traffic load is higher than roughly 60%, the most
critical time is while waiting in idle mode (idle time), since
the packets remain waiting to be served either in the CRQ
or DTQ subsystems. This might also be the reason why the

(i) The x-axis represents the relative traffic load, here
defined, as the ratio of generated data packets
per DQ-MAC superframe (i.e., MATLAB simulated
iteration). As aforementioned, the traffic load follows
a Poisson distribution, since we consider here a
generalized case scenario. In our simulated scenario,
the traffic load rises by increasing the number of
active body sensors in the BSN in each simulation.
(ii) As in the previous scenario, in the y-axis, we evaluate
the energy consumption per information bit and
the time spent in each of the aforementioned states
(i.e., transmit, receive, and idle) following DQMAC procedure in our simulated BSN scenario. The
energy consumption is computed considering each
body sensor time and power consumption in each
of these states. Thus, the energy consumption per
information bit is defined as the ratio of the average
total energy consumption per body sensor and per
payload length (i.e., information bit).
Figure 7 portrays the analytical results of the energy
consumption per information bit of the here presented
DQ-MAC model (see (5)) versus the 802.15.4 MAC model
analyzed in [19], as the relative traffic load in the system
increases. Different curves are shown for data payload
lengths (L) of 80, 100 and 120 bytes. It can be seen how
the use of DQ-MAC outperforms 802.15.4 MAC reaching
36.65% of energy-efficiency improvement, when the relative
traffic load is as high as 80%.

12

EURASIP Journal on Wireless Communications and Networking

Energy consumption per information bit (J/bit)

×10−7

Here, it must be pointed out that the time spent in
transmission mode seems constant for all traffic loads, that
is, independent of the traffic load. This is obvious for the
DTQ subsystem, since there is just one packet duration to
transmit. Now, analyzing (13), where E[NARS tx ] denotes
the average number of time frames used to transmit all
required ARS during the waiting time in the CRQ system,
we observe a dependency on arriving traffic load λ into the
CRQ subsystem, though this arriving rate becomes smaller
(for a concrete body sensor waiting in the CRQ subsystem)
with the time, that is, λ/m, λ/m2 , . . . . Furthermore, the time
spent in transmission mode is computed using expression
(7) and tARS is substantially smaller than E[tDATA ], derived
from Table 1. This explains the constant behavior of the time
spent in transmission mode.

8
7
6
5
4
3

2
1
0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Relative traffic load
Analytical (L = 80 bytes)
Analytical (L = 100 bytes)
Analytical (L = 120 bytes)

7. Conclusions

Simulated (L = 80 bytes)
Simulated (L = 100 bytes)
Simulated (L = 120 bytes)

Figure 8: DQ-MAC energy consumption per information bit:
Analytical versus Simulation.
0.1
0.09
0.08
Time (seconds)

0.07
0.06
Idle

0.05
0.04
0.03
0.02

Receive

Transmit

0.01
0
0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Relative traffic load
Analytical (L = 80 bytes)
Analytical (L = 100 bytes)

Analytical (L = 120 bytes)
Simulated

Figure 9: DQ-MAC time spent in transmit, receive and idle mode:
Analytical versus Simulated curves.

In this paper, we have been evaluating IEEE 802.15.4 MAC
limitations under new challenging healthcare requirements
for wireless body sensor networks (BSNs). Further, a new
energy-efficiency theoretical analysis for an enhanced distributed queuing medium access control (DQ-MAC) protocol has been introduced, as a potential candidate for future
BSNs. For that purpose, an energy-saving DQ-MAC superframe optimization has been presented taking energy-aware
radio activation policies into account. This allows body sensors a power management regulation to minimize the energy
consumption per information bit. The analytical study has
been compared with a BSN state-of-the-art MAC protocol
(BSN-MAC) and validated by simulation results, which have

shown that the proposed mechanism outperforms IEEE
802.15.4 MAC and BSN-MAC energy-efficiency for all traffic
loads in a generalized BSN scenario. This favorable energyefficient behavior is especially achieved thanks to the inherent protocol performance at eliminating collisions in data
transmissions, while minimizing the control overhead and
hence the overall energy consumption per information bit.

Acknowledgments
This work was performed while B. Otal was at CTTC and
was partially funded by the Research Projects NEWCOM++
(ICT-216715), CENTENO (TEC2008-06817-C02-02), and
COOLNESS (218163-FP7-PEOPLE-2007-3-1-IAPP).

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increases because of the protocol control information. This
corroborates the previous FBP assumption, in which it was
discussed that FBP should be kept fixed in size and minimized in control information, that is, to reduce energy consumption for information bit, especially in high traffic loads.

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